inst/markdown/archive/snowcrab_presentation_general_summary.md
In jae0/snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.
title: "Snow Crab, Scotian Shelf, Canada (NAFO Div. 4VWX) in 2023"
subtitle: "General Summary"
author: "Jae S. Choi"
footnote: "jae.choi@dfo-mpo.gc.ca"
institute: "Bedford Institute of Oceanography, DFO Science"
date: "r format(Sys.time(), '%d %B, %Y')"
output:
beamer_presentation:
theme: "metropolis"
colortheme: "seagull"
fonttheme: "professionalfonts"
fig_caption: yes
# latex_engine: pdflatex
latex_engine: lualatex
keep_tex: true
classoption:
- aspectratio=169 #16:9 wide
- t # top align
header-includes:
- \usepackage{graphicx}
- \usepackage[font={scriptsize}, labelfont={bf}]{caption}
# - \usepackage{float}
# - \usepackage{subfig}
# - \newcommand{\btiny}{\begin{tiny}}
# - \newcommand{\etiny}{\end{tiny}}
params:
year.assessment: 2023
media_loc: "media"
debugging: FALSE
```{r setup, include=FALSE}
require(knitr)
knitr::opts_chunk$set(
root.dir = data_root,
echo = FALSE,
out.width="6.2in",
dev.args = list(type = "cairo"),
fig.retina = 2,
dpi=192
)
# inits and data loading (front load all required data)
require(aegis)
year.assessment = params$year.assessment
year_previous = year.assessment - 1
p = bio.snowcrab::load.environment( year.assessment=year.assessment )
SCD = project.datadirectory("bio.snowcrab")
media_loc = params$media_loc
# fishery_model_results = file.path( "/home", "jae", "projects", "dynamical_model", "snowcrab", "outputs" )
fishery_model_results = file.path( SCD, "fishery_model" )
sn_env = snowcrab_load_key_results_to_memory( year.assessment, debugging=params$debugging, return_as_list=TRUE )
attach(sn_env)
# predator diet data
diet_data_dir = file.path( SCD, "data", "diets" )
require(data.table) # for speed
require(lubridate)
require(stringr)
require(gt) # table formatting
library(janitor)
require(ggplot2)
require(aegis) # map-related
require(bio.taxonomy) # handle species codes
# assimilate the CSV data tables:
# diet = get_feeding_data( diet_data_dir, redo=TRUE ) # if there is a data update
diet = get_feeding_data( diet_data_dir, redo=FALSE )
tx = taxa_to_code("snow crab")
# matching codes are
# spec tsn tx vern tx_index
#1 528 172379 BENTHODESMUS BENTHODESMUS 1659
#2 2522 98427 CHIONOECETES SPIDER QUEEN SNOW UNID 728
#3 2526 98428 CHIONOECETES OPILIO SNOW CRAB QUEEN 729
# 2 and 3 are correct
snowcrab_predators = diet[ preyspeccd %in% c(2522, 2526), ] # n=159 oservations out of a total of 58287 observations in db (=0.28% of all data)
snowcrab_predators$Species = code_to_taxa(snowcrab_predators$spec)$vern
snowcrab_predators$Predator = factor(snowcrab_predators$Species)
counts = snowcrab_predators[ , .(Frequency=.N), by=.(Species)]
setorderv(counts, "Frequency", order=-1)
# species composition
psp = speciescomposition_parameters( yrs=p$yrs, carstm_model_label="default" )
pca = speciescomposition_db( DS="pca", p=psp )
pcadata = as.data.frame( pca$loadings )
pcadata$vern = stringr::str_to_title( taxonomy.recode( from="spec", to="taxa", tolookup=rownames( pcadata ) )$vern )
# bycatch summaries
o_cfaall = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfaall" )
o_cfanorth = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfanorth" )
o_cfasouth = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfasouth" )
o_cfa4x = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfa4x" )
## Components of Snow Crab status in Maritimes Region {.c}
- Life history
- Ecosystem change
- Human interactions
- Fishery statistics
- Other interactions
- Scientific survey results
- Life history X Ecosystem change X Human interactions = Complexity
# Life history {.c}
## Life history {.c}
```{r photos, echo=FALSE, out.width='30%', fig.align='center', fig.show='hold', fig.cap = 'Snow Crab pelagic Zoea, benthic male and mating pair. Note sexual dimorphism.' }
fn1=file.path( media_loc, "snowcrab_zoea.png" )
fn2=file.path( media_loc, "snowcrab_male.png" )
fn3=file.path( media_loc, "snowcrab_male_and_female.png" )
knitr::include_graphics( c(fn1, fn2, fn3) )
# \@ref(fig:photos)
Life history: stages{.c}
```{r lifehistory, echo=FALSE, out.width='90%', fig.align='center', fig.cap = 'Life history patterns of snow crab and approximate timing of the main life history stages of snow crab and size (carapace width; CW mm) and instar (Roman numerals). Size and timings are specific to the area of study and vary with environmental conditions, food availability and genetic variability.' }
loc = file.path( Sys.getenv("HOME"), "projects", "dynamical_model", "snowcrab", "media" )
fn1=file.path( loc, "life_history.png" )
knitr::include_graphics( fn1 )
\@ref(fig:lifehistory)
## Life history: male growth stanzas {.c}
```{r lifehistory_male, echo=FALSE, out.width='50%', fig.align='center', fig.cap = 'The growth stanzas of the male component and decision paths to maturity and terminal moult. Black ellipses indicate terminally molted animals.' }
loc = file.path( Sys.getenv("HOME"), "projects", "dynamical_model", "snowcrab", "media" )
fn1=file.path( loc, "life_history_male.png" )
knitr::include_graphics( fn1 )
# \@ref(fig:lifehistory_male)
Life history: growth modes{.c}
```{r growth_modes, echo=FALSE, out.width='40%', fig.align='center', fig.cap = 'Modal analysis.' }
loc = file.path( Sys.getenv("HOME"), "bio.data", "bio.snowcrab", "output" )
fn1=file.path( loc, "size_structure", "growth_summary.png" )
knitr::include_graphics( c(fn1) )
\@ref(fig:lifehistory_male)
## Life history: notable traits
- Sexual dimorphism
- Pelagic in larval stages; benthic in pre-adolescent and adult stages
- Biannual, annual molts depending upon size/age and environmental conditions
- Terminal molt to maturity and survives for up to 5 years
- Total life span: up to 15 years.
- Stenothermic (narrow) temperature requirements < 6 C
- Ontogenetic shifts in habitat preferences: warmer, complex substrates to mud.
- Cannibalism of immature crab by mature female snow crab are known
- Primiparous female (57.4 mm CW) produces between 35,000 to 46,000 eggs
- Multiparous females more fecund (>100,000 eggs)
- Eggs extruded February and April and brooded for up to two years (> 80% in SSE follow an annual cycle) and hatched/released from April to June.
## Life history: movement (mark-recapture) {.c}
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
```{r movementtracks, echo=FALSE, out.width='60%', fig.align='center', fig.show='hold', fig.cap = 'Snow Crab movement tracks from mark and recapture.' }
fn1=file.path( media_loc, "movement0.png" )
fn2=file.path( media_loc, "movement.png" )
knitr::include_graphics( c(fn1, fn2) )
# \@ref(fig:movementtracks)
\end{column}
\begin{column}{.48\textwidth}
```{r movement, echo=FALSE, out.width='55%', fig.align='center', fig.show='hold', fig.cap = 'Snow Crab movement distances and rates.' }
fn1=file.path( media_loc, "snowcrab_movement_distances.png" )
fn2=file.path( media_loc, "snowcrab_movement_rates.png" )
knitr::include_graphics( c(fn1, fn2) )
\@ref(fig:movement)
\end{column}
\end{columns}
\end{small}
## Life history: clustering {.c}
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
```{r aggregation, echo=FALSE, out.width='60%', fig.align='center', fig.show='hold', fig.cap = 'Australian spider crab \\emph{Leptomithrax gaimardii} aggregation for moulting and migration and Alaska red king crab \\emph{Paralithodes camtschaticus} aggregation in Alaska for egg release, migrations.' }
fn1=file.path( media_loc, "australian_leptomithrax_gaimardii.png" )
fn2=file.path( media_loc, "kingcrab_aggregation.png" )
knitr::include_graphics( c(fn1, fn2 ) )
# \@ref(fig:aggregation)
-
Other crab species show "Mounding" for protection from predation (larval, moulting and females)
-
Narrow habitat preferences force them to move and cluster when environment is poor
\end{column}
\begin{column}{.48\textwidth}
```{r clustering, echo=FALSE, out.width='90%', fig.align='center', fig.show='hold', fig.cap = 'High density locations of Snow Crab, approximately 1 per square meter.'}
fn = file.path(p$project.outputdir, "maps", "map_highdensity_locations.png" )
knitr::include_graphics( fn )
\@ref(fig:aggregation)
if (0) {
# high density locations directly from databases
M = snowcrab.db( DS="set.complete", p=p )
setDT(M)
i = which(M$totno.all > 2.5*10^5)
H = M[i, .( plon, plat, towquality, dist, distance, surfacearea, vessel, yr, z, julian, no.male.all, no.female.all, cw.mean, totno.all, totno.male.imm, totno.male.mat, totno.female.imm, totno.female.mat, totno.female.primiparous, totno.female.multiparous, totno.female.berried)]
H$log10density = log10(H$totno.all)
library(ggplot2)
cst = coastline_db( p=p, project_to=st_crs(pg) )
isodepths = c(100, 200, 300)
isob = isobath_db( DS="isobath", depths=isodepths, project_to=st_crs(pg))
isob$level = as.factor( isob$level)
plt = ggplot() +
geom_sf( data=cst, show.legend=FALSE ) +
geom_sf( data=isob, aes( alpha=0.1, fill=level), lwd=0.1, show.legend=FALSE) +
geom_point(data=H, aes(x=plon, y=plat, colour=log10density), size=5) +
coord_sf(xlim = c(270, 940 ), ylim = c(4780, 5200 )) +
theme(legend.position="inside", legend.position.inside=c(0.08, 0.8))
png(filename=fn, width=1000,height=600, res=144)
(plt)
dev.off()
}
\begin{block}{Uncertainty}
Historical snow crab high density locations
\end{block}
\end{column}
\end{columns}
\end{small}
# Ecosystem change
## Ecosystem change: Predators {.c}
- SSE: many species changes, snow crab are long-lived so interact with many of them
- Stomach samples: Atlantic Halibut, Atlantic Wolffish, Thorny Skate as primary predators
- Skewed sex ratios (few mature females): possibly linked to differential predation mortality (mature females being much smaller and full of fat-rich gonads and eggs).
- Halibut (DFO 2018) have significantly increased in abundance in the Region.
- Elevated co-ocurrence in areal **densities** with snow crab trawl samples means greater encounter rates
## Ecosystem change: Predators - Atlantic Halibut {.c}
```{r halibut-timeseries, out.width='50%', echo=FALSE, fig.align='center', fig.cap = 'Atlantic Halibut crude, unadjusted geometric mean numerical density (no/km$^2$) from annual Snow Crab survey. Error bars are 95\\% Confidence Intervals.' }
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.30.pdf') )
# \@ref(fig:halibut-timeseries)
Ecosystem change: Predators - Atlantic Halibut ... {.c}
```{r halibut-map, out.width='32%', fig.show='hold', fig.align='center', fig.cap= 'Halibut density log10(no/km$^2$) from the Snow Crab survey.' }
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.30' )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( 'ms.no.30', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'ms.no.30', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'ms.no.30', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'ms.no.30', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:halibut-map)
\begin{block}{Uncertainty}
Higher predation mortality seems likely (more encounters with warmer-water species)
\end{block}
## Ecosystem change: Predators - Thorny skate {.c}
```{r thornyskate-timeseries, out.width='60%', echo=FALSE, fig.align='center', fig.cap = 'Thorny Skate crude, unadjusted geometric mean numerical density (no/km$^2$) from annual Snow Crab survey. Error bars are 95\\% Confidence Intervals.'}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.201.pdf') )
# \@ref(fig:thornyskate-timeseries)
Ecosystem change: Predators - Thorny skate ... {.c}
```{r thornyskate-map, out.width='32%', fig.show='hold', fig.align='center', fig.cap= 'Thorny skate density log10(no/km$^2$) from the Snow Crab survey.' }
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.201' )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( 'ms.no.201', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'ms.no.201', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'ms.no.201', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'ms.no.201', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:thornyskate-map)
\begin{block}{Uncertainty}
Higher predation mortality seems likely (more encounters with warmer-water species)
\end{block}
<!--
~~~{=comment}
## Ecosystem change: Predators - Striped Atlantic Wolffish {.c}
```{r Wolffish-timeseries, out.width='60%', echo=FALSE, fig.align='center', fig.cap = 'Striped Atlantic Wolffish crude, unadjusted geometric mean numerical density (no/km$^2$) from annual Snow Crab survey. Error bars are 95\\% Confidence Intervals.' }
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.50.pdf') )
# \@ref(fig:Wolffish-timeseries)
Ecosystem change: Predators - Striped Atlantic Wolffish ... {.c}
```{r Wolffish-map, out.width='32%', fig.show='hold', fig.align='center', fig.cap= 'Striped Atlantic Wolffish density log10(no/km$^2$) from the Snow Crab survey.' }
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.50' )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( 'ms.no.50', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'ms.no.50', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'ms.no.50', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'ms.no.50', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:Wolffish-map)
## Ecosystem change: Competitor - Lesser toad crab {.c}
```{r lessertoadcrab-timeseries, out.width='60%', echo=FALSE, fig.align='center', fig.cap = 'Lesser Toad Crab crude, unadjusted geometric mean numerical density (no/km$^2$) from annual Snow Crab survey. Error bars are 95\\% Confidence Intervals.' }
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.2521.pdf') )
# \@ref(fig:lessertoadcrab-timeseries)
Ecosystem change: Competitor - Lesser toad crab ... {.c}
```{r lessertoadcrab-map, out.width='32%', fig.show='hold', fig.align='center', fig.cap= 'Lesser Toad Crab density log10(no/km$^2$) from the Snow Crab survey.' }
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.2521' )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( 'ms.no.2521', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'ms.no.2521', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'ms.no.2521', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'ms.no.2521', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:lessertoadcrab-map)
~~~
-->
## Ecosystem change: Co-occurring - Northern shrimp {.c}
```{r Shrimp-timeseries, out.width='60%', echo=FALSE, fig.align='center', fig.cap = 'Northern Shrimp crude, unadjusted geometric mean numerical density (n/$km^2$) from annual Snow Crab survey. Error bars are 95\\% Confidence Intervals.' }
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.2211.pdf') )
# \@ref(fig:Shrimp-timeseries)
Ecosystem change: Co-occurring - Northern shrimp ... {.c}
```{r Shrimp-map, out.width='32%', fig.show='hold', fig.align='center', fig.cap= 'Northern Shrimp density log10(no/km$^2$) from the Snow Crab survey.' }
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.2211' )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( 'ms.no.2211', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'ms.no.2211', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'ms.no.2211', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'ms.no.2211', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:Shrimp-map)
Shrimp with similar habitat preferences have declined, possibly due to large-scaled habitat variations and predation.
\begin{block}{Uncertainty}
Sampling was incomplete in 2020 and 2022 in S-ENS.
\end{block}
## Ecosystem change: species composition
```{r speciesomposition, echo=FALSE, out.width='48%', fig.align='center', fig.show='hold', fig.cap = 'Species composition in space and time. Primary gradient is related to bottom temperatures.' }
spc_loc = file.path( data_root, 'aegis', 'speciescomposition', 'modelled', 'default', 'maps' )
fn1 = file.path( spc_loc, 'pca1.space_re_total.png')
fn2 = file.path( spc_loc, 'pca2.space_re_total.png')
ts_loc = file.path( data_root, 'aegis', 'speciescomposition', 'modelled', 'default', 'figures' )
fn3 = file.path( ts_loc, 'pca1_time.png')
fn4 = file.path( ts_loc, 'pca2_time.png')
knitr::include_graphics( c(fn1, fn3 ) )
# \@ref(fig:habitat3)
\begin{block}{Uncertainty}
Sampling was incomplete in 2020 and 2022 in S-ENS.
\end{block}
Ecosystem change: Bottom Temperature {.c}
```{r bottom-temperatures-survey, out.width='50%', echo=FALSE, fig.align='center', fig.cap = 'Annual variations in bottom temperature observed during the Snow Crab survey. The horizontal (black) line indicates the long-term, median temperature within each subarea. Error bars represent standard errors.' }
knitr::include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 't.pdf') )
\@ref(fig:bottom-temperatures-survey)
\begin{block}{Uncertainty}
Sampling was incomplete in 2020 and 2022 in S-ENS.
\end{block}
## Ecosystem change: Bottom Temperature ... {.c}
- Average bottom temperatures **observed** in the 2022 Snow Crab survey were near or above historical highs in all areas
- Temperatures are more stable in N-ENS than S-ENS; 4X exhibits the most erratic and highest annual mean bottom temperatures.
- Observed temperatures in the 2022 Snow Crab survey for S-ENS increased well above the average.
- Average temperature increased well beyond the $7^\circ$C threshold in 4X. N-ENS and S-ENS also continued to experience historical highs in bottom temperature and elevated spatial variability of bottom temperatures.
## Ecosystem considerations: Bottom Temperature ... {.c}
```{r bottom-temperatures-map, out.width='30%', echo=FALSE, fig.show='hold', fig.align='center', fig.cap = 'Spatial variations in bottom temperature estimated from a historical analysis of temperature data for 1 September.' }
loc = file.path( data_root, 'aegis', 'temperature', 'modelled', 'default', 'maps' )
yrsplot = year.assessment + c(0:-10)
fn10 = file.path( loc, paste( 'predictions.', yrsplot[10], '.0.75', '.png', sep='') )
fn9 = file.path( loc, paste( 'predictions.', yrsplot[9], '.0.75', '.png', sep='') )
fn8 = file.path( loc, paste( 'predictions.', yrsplot[8], '.0.75', '.png', sep='') )
fn7 = file.path( loc, paste( 'predictions.', yrsplot[7], '.0.75', '.png', sep='') )
fn6 = file.path( loc, paste( 'predictions.', yrsplot[6], '.0.75', '.png', sep='') )
fn5 = file.path( loc, paste( 'predictions.', yrsplot[5], '.0.75', '.png', sep='') )
fn4 = file.path( loc, paste( 'predictions.', yrsplot[4], '.0.75', '.png', sep='') )
fn3 = file.path( loc, paste( 'predictions.', yrsplot[3], '.0.75', '.png', sep='') )
fn2 = file.path( loc, paste( 'predictions.', yrsplot[2], '.0.75', '.png', sep='') )
fn1 = file.path( loc, paste( 'predictions.', yrsplot[1], '.0.75', '.png', sep='') )
knitr::include_graphics( c( fn3, fn2, fn1) )
# \@ref(fig:bottom-temperatures-map)
# *Spatial variations in bottom temperature estimated from a historical analysis of temperature data for 1 September.*
\begin{block}{Uncertainty}
* Groundfish surveys were not conducted in 2020 and 2022 in the snow crab domain.
- Snow crab surveys were not conducted in 2020, and incomplete in 2022 for S-ENS.
\end{block}
Ecosystem considerations: Bottom Temperature ... {.c}
Persistent spatial gradient of almost $15^\circ$C in bottom temperatures in the Maritimes Region.
Variable due to confluence of the warm, high salinity Gulf Stream from the S-SE along the shelf edge; cold, low salinity Labrador Current; and cold low salinity St. Lawrence outflow from the N-NE, as well as a nearshore Nova Scotia current, running from the NE.
```{r bottom-temperatures-spatialeffect, out.width='35%', echo=FALSE, fig.align='center', fig.cap = 'Persistent spatial effect of bottom temperature, relative to the overall mean, after adjustment for spatiotemporal variability and autocorrelations. Time period from 1999 to present.' }
loc = file.path( data_root, 'aegis', 'temperature', 'modelled', 'default', 'maps' )
knitr::include_graphics( file.path( loc, 'space_re_total.png') )
\@ref(fig:bottom-temperatures-spatialeffect)
## Ecosystem considerations: Bottom Temperature ... {.c}
\begin{columns}
\begin{column}{.6\textwidth}
```{r bottom-temperatures, out.width='65%', echo=FALSE, fig.align='center', fig.cap = '' }
knitr::include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'temperature_bottom.pdf') )
# \@ref(fig:bottom-temperatures)
\end{column}
\begin{column}{.4\textwidth}
\vspace{12mm}
\begin{footnotesize}
\textbf{Figure}: Temporal variations in bottom temperature estimated from a historical analysis of temperature data. Red horizontal line is at $7^\circ$C. Presented are 95\% Credible Intervals of spatial variability in temperature at each time slice, after adjustment for spatiotemporal autocorrelation.
\end{footnotesize}
\end{column}
\end{columns}
Human interactions
Human interactions: Every known population is exploited worldwide {.c}
- Human consumption (males; > 95mm CW) due to higher meat yield and sexual dimorphism
- Bait in other fisheries (illegal in Canada, but still present)
- Fertilizer in agriculture
- Chitosan: glucosamine polysaccharide derived from chitin
- agent to slow bleeding from wounds (Zhang et al. 2015, Moghadas et al. 2016)
- agriculturally as natural fungicides and bactericides (Linden & Stoner 2007)
- plastic replacement (Tampieri et al. 2003)
- battery electrolyte (Poosapati et al. 2021).
Human interactions: Management Approach
\begin{columns}
\begin{column}{.46\textwidth}
\begin{tiny}
```{r area_map, echo=FALSE, out.width='80%', fig.align='center', fig.cap = 'The Scotian Shelf (NW Atlantic Ocean; NAFO Div. 4VWX). Shown are isobaths and major bathymetric features. Managed Crab Fishing Areas (CFAs; divided by dashed lines) include: NENS, SENS, 4X. SENS is further subdivided (dotted line) into 23 (NW) and 24 (SE).' }
loc = file.path( Sys.getenv("HOME"), "projects", "dynamical_model", "snowcrab", "media" )
fn1=file.path( loc, "area_map.png" )
knitr::include_graphics( fn1 )
\@ref(fig:area_map)
\end{tiny}
\end{column}
\begin{column}{.52\textwidth}
\begin{footnotesize}
\begin{itemize}
\item Precautionary Approach, Fish Stock Provisions, 2022
\item Spatial refugia (slope edge, MPAs)
\item Temporal refugia (fishing seasons)
\item Biological refugia: most life stages protected
\begin{itemize}
\begin{scriptsize}
\item Conservative exploitation since mid-2000s
\item Spawning stock legally and completely protected
\item Market-driven protection for 10+ yrs
\end{scriptsize}
\end{itemize}
\item Evidence-based decision making: trawl survey, assessment
\item Distributed knowledge network: traditional, historical, scientific
\item Satellite VMS; biodegradeable mesh (ghost-fishing); weighted lines (entanglement), etc ...
\end{itemize}
\end{footnotesize}
\end{column}
\end{columns}
## Human interactions: Fishing effort {.c}
Similar between `r year.assessment` and `r year_previous` in terms of spatial distribution. In S-ENS, there was, however, a minor spatial contraction to inshore areas and away from the area 23-24 boundary.
\begin{tiny}
```{r effort-map, echo=FALSE, out.width='45%', fig.show='hold', fig.align='center', fig.cap = 'Snow Crab fishing effort from fisheries logbook data for previous and current years. Units are No. $\\times 10^3$ per (10 km X 10 km) grid.' }
loc0= file.path( SCD, "output", "maps", "logbook", "snowcrab", "annual", "effort" )
fn1 = file.path( loc0, paste( "effort", year_previous, "png", sep=".") )
fn2 = file.path( loc0, paste( "effort", year.assessment, "png", sep=".") )
include_graphics( c(fn1, fn2) )
# \@ref(fig:landings-map)
\end{tiny}
Human interactions: Fishing effort ... {.c}
\begin{tiny}
```{r effort-timeseries, echo=FALSE, out.width='60%', fig.align='center', fig.cap = 'Temporal variations in fishing effort $\times 10^3$ trap hauls.' }
fn1=file.path( SCD, "assessments", year.assessment, "timeseries", "fishery", "effort.ts.pdf" )
knitr::include_graphics( fn1 )
\@ref(fig:effort-timeseries)
\end{tiny}
<!--
```{r table-fishery-nens, echo=FALSE, eval = FALSE }
ii = which(dt$Region=="cfanorth")
oo = dt[ii, c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
kable( oo, format="simple", row.names=FALSE, align="cccccc",
caption = "Fishery performance statistics in N-ENS. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th).")
# \@ref(tab:table-fishery-nens)
```
---
```{r table-fishery-sens, echo=FALSE, eval = FALSE }
ii = which(dt$Region=="cfasouth")
oo = dt[ii, c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
kable( oo, format="simple", row.names=FALSE, align="cccccc",
caption = "Fishery performance statistics in S-ENS. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th).")
# \@ref(tab:table-fishery-nens)
```
---
```{r table-fishery-4x, echo=FALSE, eval = FALSE }
ii = which(dt$Region=="cfa4x")
oo = dt[ii,c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
kable(oo, format="simple", row.names=FALSE, align="cccccc",
caption = "Fishery performance statistics in 4X. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th). There were no landings or TACs in 2018/2019 due to indications of low abundance.")
# \@ref(tab:table-fishery-4x)
```
-->
## Human interactions: Fishery landings and TACs {.c}
- The landings in N-ENS for 2022 and 2021 were similar in their spatial patterns.
- The landings in 4X for 2022 were spatially more contracted than 2021.
\begin{tiny}
```{r landings-map, echo=FALSE, out.width='45%', fig.show='hold', fig.align='center', fig.cap = 'Snow Crab landings from fisheries logbook data for previous and current years. Units are tons per 10 km x 10 km grid.' }
loc0= file.path( SCD, "output", "maps", "logbook", "snowcrab", "annual", "landings" )
fn1 = file.path( loc0, paste( "landings", year_previous, "png", sep=".") )
fn2 = file.path( loc0, paste( "landings", year.assessment, "png", sep=".") )
knitr::include_graphics( c(fn1, fn2 ) )
# \@ref(fig:landings-map)
\end{tiny}
Human interactions: Fishery landings and TACs ... {.c}
In 2022, landings in all areas were below respective TACs.
\begin{tiny}
```{r landings-timeseries, echo=FALSE, out.width='60%', fig.align='center', fig.cap = 'Landings (t) of Snow Crab on the SSE. For 4X, the year refers to the starting year of the season. Inset is a closeup view of the timeseries for N-ENS and 4X.'}
include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "fishery", "landings.ts.pdf" ) )
\@ref(fig:landings-timeseries)
\end{tiny}
## Human interactions: Fishery catch rates
- Generally, the spatial extent of exploitation was smaller, many of the exploited area show elevated catch rates,
- In 4X catch rates were lower in 2022.
```{r cpue-map, echo=FALSE, out.width='45%', fig.show='hold', fig.align='center', fig.cap = 'Snow Crab crude catch rates on the Scotian Shelf for previous and current years. Units are kg/trap haul per 10 km x 10 km grid.' }
loc0= file.path( SCD, "output", "maps", "logbook", "snowcrab", "annual", "cpue" )
fn1 = file.path( loc0, paste( "cpue", year_previous, "png", sep=".") )
fn2 = file.path( loc0, paste( "cpue", year.assessment, "png", sep=".") )
knitr::include_graphics( c(fn1, fn2 ) )
# \@ref(fig:cpue-map)
Human interactions: Fishery catch rates ...
\begin{tiny}
```{r cpue-timeseries, echo=FALSE, out.width='60%', fig.align='center', fig.cap = 'Temporal variations in crude catch rates of Snow Crab (kg per trap haul).'}
include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "fishery", "cpue.ts.pdf" ) )
\@ref(fig:cpue-timeseries)
\end{tiny}
## Human interactions: At-Sea-Observed information
- Target: 5% of landings
- In 2021, both N-ENS and 4X were not sampled by At-Sea-Observers.
- In 2022, ~ 0.8 % of landings in 4X were sampled by At-Sea-Observers
\begin{tiny}
```{r observer-locations-map, out.width='22%', fig.show='hold', fig.align='center', fig.cap= 'Snow Crab At-sea-observer locations.' }
loc = file.path( SCD, "output", "maps", "observer.locations" )
yrsplot = year.assessment + c(0:-4)
fn4 = file.path( loc, paste( "observer.locations", yrsplot[4], "png", sep=".") )
fn3 = file.path( loc, paste( "observer.locations", yrsplot[3], "png", sep=".") )
fn2 = file.path( loc, paste( "observer.locations", yrsplot[2], "png", sep=".") )
fn1 = file.path( loc, paste( "observer.locations", yrsplot[1], "png", sep=".") )
include_graphics( c( fn4, fn3, fn2, fn1) )
# \@ref(fig:observer-locations-map)
\end{tiny}
\vspace{5mm}
Bycatch: last assessment was in 2017 and levels were << 1% by weight.
Stock status: Carapace condition of mature male crab
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
\vspace{4mm}
- CC5 crab of mature crab.
\vspace{12mm}
\begin{scriptsize}
\textbf{Figure}: Size-frequency of mature male Snow Crab by carapace width (mm) and carapace condition from surveys. Columns are years and rows are N-ENS (top), S-ENS(middle) and 4X(bottom).
\end{scriptsize}
\end{column}
\begin{column}{.48\textwidth}
\begin{tiny}
```{r sizefeq-male-survey-cc, out.width='45%', fig.show='hold', echo=FALSE, fig.align='center', fig.cap = ''}
odir = file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "carapacecondition" )
fn1 = file.path( odir, "sizefreq.cfanorth.2019.pdf" )
fn2 = file.path( odir, "sizefreq.cfasouth.2019.pdf" )
fn3 = file.path( odir, "sizefreq.cfa4x.2019.pdf" )
fn4 = file.path( odir, "sizefreq.cfanorth.2021.pdf" )
fn5 = file.path( odir, "sizefreq.cfasouth.2021.pdf" )
fn6 = file.path( odir, "sizefreq.cfa4x.2021.pdf" )
fn7 = file.path( odir, "sizefreq.cfanorth.2022.pdf" )
fn8 = file.path( odir, "sizefreq.cfasouth.2022.pdf" )
fn9 = file.path( odir, "sizefreq.cfa4x.2022.pdf" )
include_graphics(c( fn4, fn7, fn5, fn8, fn6, fn9) )
\@ref(fig:sizefeq-male-survey-cc)
\end{tiny}
\end{column}
\end{columns}
\end{small}
## Stock status: Recruitment
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
\vspace{4mm}
\begin{itemize}
\item Little to no recruitment is expected for the next 1-3 years in N-ENS.
\item Moderate levels of recruitment are expected in S-ENS.
\item Low to moderate levels of recruitment are expected for 2 years in 4X.
\end{itemize}
\vspace{4mm}
\begin{scriptsize}
\textbf{Figure}: Size-frequency (areal density; no/km$^2$) histograms by carapace width of male Snow Crab. The vertical line represents the legal size (95 mm). Immature animals are shown with light coloured bars, mature with dark.
\end{scriptsize}
\end{column}
\begin{column}{.48\textwidth}
```{r sizefeq-male, out.width='90%', echo=FALSE, fig.align='center', fig.cap = ''}
include_graphics( file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "survey", "male.denl.png" ) )
# \@ref(fig:sizefeq-male)
\end{column}
\end{columns}
\end{small}
Stock status: Reproduction
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
\begin{itemize}
\item All areas had recruitment of female crab into the mature (egg-bearing) segment of the population from 2016-2022.
\item In N-ENS for 2022, a decline in numerical densities, and low densities of adolescent females.
\item Egg and larval production is expected to be moderate to high in the next year in all areas except N-ENS.
\end{itemize}
\vspace{2mm}
\begin{scriptsize}
\textbf{Figure}: Size-frequency (areal density; no/km$^2$) histograms by carapace width of female Snow Crab. Immature animals are shown with light coloured bars, mature with dark.
\end{scriptsize}
\end{column}
\begin{column}{.48\textwidth}
```{r sizefeq-female, out.width='90%', echo=FALSE, fig.align='center', fig.cap = ''}
include_graphics( file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "survey", "female.denl.png" ) )
\@ref(fig:sizefeq-female)
\end{column}
\end{columns}
\end{small}
## Stock status: Reproduction ...
```{r fmat-timeseries, out.width='50%', echo=FALSE, fig.align='center', fig.cap = 'Mature female density log$_{10}$(no/km$^2$) from the Snow Crab survey.' }
include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "survey", "totno.female.mat.pdf") )
# \@ref(fig:fmat-timeseries)
Stock status: Mature female
Distributions are heterogeneous and often in shallower areas.
```{r fmat-map, echo=FALSE, out.width='32%', fig.show='hold', fig.align='center', fig.cap= 'Mature female density log$_{10}$(no/km$^2$) from the Snow Crab survey.' }
loc = file.path( SCD, "output", "maps", "survey", "snowcrab", "annual", "totno.female.mat" )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( "totno.female.mat", yrsplot[4], "png", sep=".") )
fn3 = file.path( loc, paste( "totno.female.mat", yrsplot[3], "png", sep=".") )
fn2 = file.path( loc, paste( "totno.female.mat", yrsplot[2], "png", sep=".") )
fn1 = file.path( loc, paste( "totno.female.mat", yrsplot[1], "png", sep=".") )
include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:fmat-map)
## Stock status: Viable Habitat {.t}
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
```{r habitat, echo=FALSE, out.width='52%', fig.align='center', fig.show='hold', fig.cap = 'Persistent habitat.' }
fn1=file.path( media_loc, "viable_habitat.png" )
knitr::include_graphics( c(fn1 ) )
# \@ref(fig:habitat)
\end{column}
\begin{column}{.48\textwidth}
```{r habitat2, echo=FALSE, out.width='40%', fig.align='center', fig.show='hold', fig.cap = 'Habitat preferences.' }
fn2=file.path( media_loc, "viable_habitat_depth_temp.png" )
knitr::include_graphics( c( fn2 ) )
\@ref(fig:habitat2)
\end{column}
\end{columns}
\end{small}
<!--
## Stock status: Viable Habitat ... {.t}
\vspace{6mm}
```{r habitat3, echo=FALSE, eval = FALSE, out.width='30%', fig.align='center', fig.show='hold', fig.cap = 'Persistent habitat.' }
fn1=file.path( media_loc, "vh_i.png" )
fn2=file.path( media_loc, "vh_mf.png" )
fn3=file.path( media_loc, "vh_mm.png" )
knitr::include_graphics( c(fn1, fn2, fn3 ) )
# \@ref(fig:habitat3)
-->
Stock status: Viable Habitat ... {.c}
```{r fb-habitat-map, out.width='30%', fig.show='hold', fig.align='center', fig.cap= 'Habitat viability (probability; fishable Snow Crab).' }
loc = file.path( SCD, 'modelled', 'default_fb', 'predicted_habitat' )
vn = "habitat."
yrsplot = year.assessment + c(0:-10)
fn10 = file.path( loc, paste( vn, yrsplot[10], '.png', sep='') )
fn9 = file.path( loc, paste( vn, yrsplot[9], '.png', sep='') )
fn8 = file.path( loc, paste( vn, yrsplot[8], '.png', sep='') )
fn7 = file.path( loc, paste( vn, yrsplot[7], '.png', sep='') )
fn6 = file.path( loc, paste( vn, yrsplot[6], '.png', sep='') )
fn5 = file.path( loc, paste( vn, yrsplot[5], '.png', sep='') )
fn4 = file.path( loc, paste( vn, yrsplot[4], '.png', sep='') )
fn3 = file.path( loc, paste( vn, yrsplot[3], '.png', sep='') )
fn2 = file.path( loc, paste( vn, yrsplot[2], '.png', sep='') )
fn1 = file.path( loc, paste( vn, yrsplot[1], '.png', sep='') )
include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:fb-habitat-map)
Figure XXX. Habitat viability (probability; fishable Snow Crab)
## Stock status: Viable Habitat ... {.c}
```{r fb-habitat-timeseries, out.width='50%', echo=FALSE, fig.align='center', fig.cap = 'Habitat viability (probability; fishable Snow Crab). Means and 95\\% Credible Intervals are presented.' }
loc = file.path( SCD, 'modelled', 'default_fb', 'aggregated_habitat_timeseries' )
include_graphics( file.path( loc, 'habitat_M0.png') )
# \@ref(fig:fb-habitat-timeseries)
Stock status: Sex ratios (proportion female, mature)
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
\vspace{2mm}
\begin{itemize}
\item Mostly male-dominated: larger size may be protective against predation?
\item Imbalance indicates differential mortality: predation, competition and fishing
\item In 4X, sex ratios are balanced.
\end{itemize}
\end{column}
\begin{column}{.48\textwidth}
```{r sexratio-mature, out.width='60%', echo=FALSE, fig.align='center', fig.cap = 'Timeseries of sex ratios.' }
include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "survey", "sexratio.mat.pdf") )
\@ref(fig:sexratio-mature)
\end{column}
\end{columns}
\end{small}
```{r sexratio-map, echo=FALSE, out.width='25%', fig.show='hold', fig.align='center', fig.cap= 'Map of sex ratios.'}
yrsplot = setdiff( year.assessment + c(0:-4), 2020)
loc = file.path( SCD, "output", "maps", "survey", "snowcrab", "annual", "sexratio.mat" )
fn4 = file.path( loc, paste( "sexratio.mat", yrsplot[4], "png", sep=".") )
fn3 = file.path( loc, paste( "sexratio.mat", yrsplot[3], "png", sep=".") )
fn2 = file.path( loc, paste( "sexratio.mat", yrsplot[2], "png", sep=".") )
fn1 = file.path( loc, paste( "sexratio.mat", yrsplot[1], "png", sep=".") )
include_graphics( c( fn3, fn2, fn1 ) )
# \@ref(fig:sexratio-map)
Stock status: Biomass Density {.c}
Note that high and low biomass density areas fluctuate with time
\begin{tiny}
```{r fbgeomean-map, echo=FALSE, out.width='30%', fig.show='hold', fig.align='center', fig.cap= 'Snow Crab survey fishable component biomass density log~10(t/km$^2$). Note, there is no data in 2020.' }
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'R0.mass')
yrsplot = setdiff(year.assessment + c(0:-9), 2020 )
fn6 = file.path( loc, paste( 'R0.mass', yrsplot[6], 'png', sep='.') )
fn5 = file.path( loc, paste( 'R0.mass', yrsplot[5], 'png', sep='.') )
fn4 = file.path( loc, paste( 'R0.mass', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'R0.mass', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'R0.mass', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'R0.mass', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
\end{tiny}
## Stock status: Biomass Density ... {.c}
- A peak in 2009 to 2014 and has since been declining in all areas.
\begin{tiny}
```{r fbGMTS, out.width='50%', echo=FALSE, fig.align='center', fig.cap = 'The crude, unadjusted geometric mean fishable biomass density log~10(t/km$^2$) from the Snow Crab survey. Error bars represent 95\\% Confidence Intervals. Note the absence of data in 2020. Prior to 2004, surveys were conducted in the Spring.'}
fn = file.path(SCD,'assessments', year.assessment, 'timeseries','survey','R0.mass.pdf')
include_graphics( c(fn) )
#\@ref(fig:fbGMTS)
\end{tiny}
Stock status: Biomass Index (aggregate)
A contraction of spatial range in 4X and the western parts of S-ENS were also evident in 2021 to 2022.
```{r fbindex-map, echo=FALSE, out.width='30%', fig.show='hold', fig.align='center', fig.cap= 'Biomass index log~10(t/km$^2$) predicted from the Snow Crab survey.' }
loc = file.path( SCD, 'modelled', 'default_fb', 'predicted_biomass_densities' )
yrsplot = year.assessment + c(0:-10)
fn10 = file.path( loc, paste( 'biomass', yrsplot[10], 'png', sep='.') )
fn9 = file.path( loc, paste( 'biomass', yrsplot[9], 'png', sep='.') )
fn8 = file.path( loc, paste( 'biomass', yrsplot[8], 'png', sep='.') )
fn7 = file.path( loc, paste( 'biomass', yrsplot[7], 'png', sep='.') )
fn6 = file.path( loc, paste( 'biomass', yrsplot[6], 'png', sep='.') )
fn5 = file.path( loc, paste( 'biomass', yrsplot[5], 'png', sep='.') )
fn4 = file.path( loc, paste( 'biomass', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'biomass', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'biomass', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'biomass', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
## Stock status: Biomass Index (aggregate) ... {.c}
\begin{tiny}
```{r fbindex-timeseries, out.width='60%', echo=FALSE, fig.align='center', fig.cap = 'The fishable biomass index (t) predicted by CARSTM of Snow Crab survey densities. Error bars represent Bayesian 95\\% Credible Intervals. Note large errors in 2020 when there was no survey.' }
include_graphics( file.path( SCD, 'modelled', 'default_fb', 'aggregated_biomass_timeseries' , 'biomass_M0.png') )
# \@ref(fig:fbindex-timeseries)
\end{tiny}
Stock status: Modelled Biomass (pre-fishery) ... {.c}
\begin{tiny}
```{r logisticPredictions, out.width='32%', echo=FALSE, fig.show='hold', fig.align='center', fig.cap = 'Model 1 fishable, posterior mean modelled biomass (pre-fishery; kt) are shown in dark orange for N-ENS, S-ENS and 4X (left, middle and right). Light orange are posterior samples of modelled biomass (pre-fishery; kt) to illustrate the variability of the predictions. The biomass index (post-fishery, except prior to 2004) after model adjustment by the model catchability coefficient is in gray.' }
loc = file.path( SCD, 'fishery_model', year.assessment, 'logistic_discrete_historical' )
fn1 = file.path( loc, 'plot_predictions_cfanorth.pdf' )
fn2 = file.path( loc, 'plot_predictions_cfasouth.pdf' )
fn3 = file.path( loc, 'plot_predictions_cfa4x.pdf' )
include_graphics(c(fn1, fn2, fn3) )
\@ref(fig:logisticPredictions)
\end{tiny}
<!--
## Stock status: Fishing Mortality {.c}
N-ENS: #r round(FM_north[t0],3)` (annual exploitation rate of #r round(100*(exp(FM_north[t0])-1),2)`%) in #r year.assessment`
- Up from the #r year_previous` rate of #r round(FM_north[t1],3)` (annual exploitation rate of #r round(100*(exp(FM_north[t1])-1),1)`%)
S-ENS: #r round(FM_south[t0],3)` (annual exploitation rate of #r round(100*(exp(FM_south[t0])-1),1)`%) in #r year.assessment`
- Decreasing marginally from the #r year_previous` rate of #r round(FM_south[t1],3)` (annual exploitation rate of #r round(100*(exp(FM_south[t1])-1),1)`%)
4X: #r round(FM_4x[t0],3)` (annual exploitation rate of #r round(100*(exp(FM_4x[t0])-1),1)`%) in #r year.assessment`-#r year.assessment+1` season
- Decreasing from the #r year.assessment-1`-#r year.assessment` season rate of #r round(FM_4x[t1],3)` (annual exploitation rate of #r round(100*(exp(FM_4x[t1])-1),1)`%)
Localized exploitation rates are likely higher, as not all areas for which biomass is estimated are fished.
-->
## Stock status: Fishing Mortality ... {.c}
```{r logisticFishingMortality, out.width='32%', echo=FALSE, fig.show='hold', fig.align='center', fig.cap = 'Time-series of modelled instantaneous fishing mortality from Model 1, for N-ENS (left), S-ENS (middle), and 4X (right). Samples of the posterior densities are presented, with the darkest line being the mean.' }
odir = file.path( fishery_model_results, year.assessment, "logistic_discrete_historical" )
fn1 = file.path( odir, "plot_fishing_mortality_cfanorth.pdf" )
fn2 = file.path( odir, "plot_fishing_mortality_cfasouth.pdf" )
fn3 = file.path( odir, "plot_fishing_mortality_cfa4x.pdf" )
include_graphics(c(fn1, fn2, fn3) )
# \@ref(fig:logisticFishingMortality)
Stock status: Reference Points {.c}
```{r ReferencePoints, out.width='40%', echo=FALSE, fig.align='center', fig.cap = 'Harvest control rules for the Scotian Shelf Snow Crab fisheries.' }
include_graphics( file.path( params$media_loc, 'harvest_control_rules.png') )
\@ref(fig:ReferencePoints)
## Stock status: Reference Points ... {.c}
```{r logistic-hcr, out.width='32%', echo=FALSE, fig.show='hold', fig.align='center', fig.cap = 'Reference Points (fishing mortality and modelled biomass) from Model 1, for N-ENS (left), S-ENS (middle), and 4X (right). The large yellow dot indicates most recent year and the 95\\% CI.' }
odir = file.path( fishery_model_results, year.assessment, "logistic_discrete_historical" )
fn1 = file.path( odir, 'plot_hcr_cfanorth.pdf' )
fn2 = file.path( odir, 'plot_hcr_cfasouth.pdf' )
fn3 = file.path( odir, 'plot_hcr_cfa4x.pdf' )
include_graphics(c(fn1, fn2, fn3) )
# \@ref(fig:logistic-hcr)
- N-ENS is in the "healthy" zone
- S-ENS is in the "healthy" zone
- 4X is in the "cautious" zone
References
\begin{tiny}
Banerjee, S., Carlin, B. P., and Gelfand, A. E.. 2004. Hierarchical Modeling and Analysis for Spatial Data. Monographs on Statistics and Applied Probability. Chapman and Hall/CRC.
Besag, Julian. 1974. Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society Series B (Methodological) 1974: 192-236.
Canada Gazette. 2022. Regulations Amending the Fishery (General) Regulations. Part II, Volume 156, Number 8.
Canada Gazette. 2016. St. Anns Bank Marine Protected Area Regulations. Canada Gazette, Part I, Vol 150, Issue 51: 4143-4149.
Choi, J.S. 2020. A Framework for the assessment of Snow Crab (Chioneocete opilio) in Maritimes Region (NAFO Div 4VWX) . DFO Can. Sci. Advis. Sec. Res. Doc. 2020/nnn. v + xxx p.
Choi, J.S. 2022. Reconstructing the Decline of Atlantic Cod with the Help of Environmental Variability in the Scotian Shelf of Canada. bioRxiv. https://doi.org/10.1101/2022.05.05.490753.
Choi, J. S., and B. C. Patten. 2001. Sustainable Development: Lessons from the Paradox of Enrichment. Ecosystem Health 7: 163–77.
Choi, Jae S., B. Cameron, K. Christie, A. Glass, and E. MacEachern. 2022. Temperature and Depth Dependence of the Spatial Distribution of Snow Crab. bioRxiv. https://doi.org/10.1101/2022.12.20.520893.
\end{tiny}
References ...
\begin{tiny}
DFO. 2018. Stock Status Update of Atlantic Halibut (Hippoglossus hippoglossus) on the Scotian Shelf and Southern Grand Banks in NAFO Divisions 3NOPs4VWX5Zc. DFO Can. Sci. Advis. Sec. Sci. Resp. 2018/022.
Hebert M, Miron G, Moriyasu M, Vienneau R, and DeGrace P. Efficiency and ghost fishing of Snow Crab (Chionoecetes opilio) traps in the Gulf of St. Lawrence. Fish Res. 2001; 52(3): 143-153. 10.1016/S0165-7836(00)00259-9
Riebler, A., Sørbye, S.H., Simpson D., and Rue, H. 2016. An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical methods in medical research 25: 1145-1165.
Simpson, D., Rue, H., Riebler, A., Martins, T.G., and Sørbye, SH. 2017. Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors. Statist. Sci. 32: 1-28.
\end{tiny}
END
jae0/snowcrab documentation built on Nov. 6, 2024, 10:13 p.m.
R Package Documentation
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title: "Snow Crab, Scotian Shelf, Canada (NAFO Div. 4VWX) in 2023" subtitle: "General Summary" author: "Jae S. Choi"
footnote: "jae.choi@dfo-mpo.gc.ca"
institute: "Bedford Institute of Oceanography, DFO Science"
date: "r format(Sys.time(), '%d %B, %Y')"
output: beamer_presentation: theme: "metropolis" colortheme: "seagull" fonttheme: "professionalfonts" fig_caption: yes # latex_engine: pdflatex latex_engine: lualatex keep_tex: true classoption: - aspectratio=169 #16:9 wide - t # top align header-includes: - \usepackage{graphicx} - \usepackage[font={scriptsize}, labelfont={bf}]{caption} # - \usepackage{float} # - \usepackage{subfig} # - \newcommand{\btiny}{\begin{tiny}} # - \newcommand{\etiny}{\end{tiny}} params: year.assessment: 2023 media_loc: "media" debugging: FALSE
```{r setup, include=FALSE} require(knitr) knitr::opts_chunk$set( root.dir = data_root, echo = FALSE, out.width="6.2in",
dev.args = list(type = "cairo"),
fig.retina = 2,
dpi=192
)
# inits and data loading (front load all required data)
require(aegis)
year.assessment = params$year.assessment year_previous = year.assessment - 1 p = bio.snowcrab::load.environment( year.assessment=year.assessment ) SCD = project.datadirectory("bio.snowcrab") media_loc = params$media_loc
# fishery_model_results = file.path( "/home", "jae", "projects", "dynamical_model", "snowcrab", "outputs" ) fishery_model_results = file.path( SCD, "fishery_model" )
sn_env = snowcrab_load_key_results_to_memory( year.assessment, debugging=params$debugging, return_as_list=TRUE )
attach(sn_env)
# predator diet data diet_data_dir = file.path( SCD, "data", "diets" ) require(data.table) # for speed require(lubridate) require(stringr) require(gt) # table formatting library(janitor) require(ggplot2) require(aegis) # map-related require(bio.taxonomy) # handle species codes
# assimilate the CSV data tables: # diet = get_feeding_data( diet_data_dir, redo=TRUE ) # if there is a data update diet = get_feeding_data( diet_data_dir, redo=FALSE ) tx = taxa_to_code("snow crab") # matching codes are # spec tsn tx vern tx_index #1 528 172379 BENTHODESMUS BENTHODESMUS 1659 #2 2522 98427 CHIONOECETES SPIDER QUEEN SNOW UNID 728 #3 2526 98428 CHIONOECETES OPILIO SNOW CRAB QUEEN 729 # 2 and 3 are correct
snowcrab_predators = diet[ preyspeccd %in% c(2522, 2526), ] # n=159 oservations out of a total of 58287 observations in db (=0.28% of all data) snowcrab_predators$Species = code_to_taxa(snowcrab_predators$spec)$vern snowcrab_predators$Predator = factor(snowcrab_predators$Species)
counts = snowcrab_predators[ , .(Frequency=.N), by=.(Species)] setorderv(counts, "Frequency", order=-1)
# species composition psp = speciescomposition_parameters( yrs=p$yrs, carstm_model_label="default" ) pca = speciescomposition_db( DS="pca", p=psp )
pcadata = as.data.frame( pca$loadings ) pcadata$vern = stringr::str_to_title( taxonomy.recode( from="spec", to="taxa", tolookup=rownames( pcadata ) )$vern )
# bycatch summaries o_cfaall = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfaall" ) o_cfanorth = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfanorth" ) o_cfasouth = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfasouth" ) o_cfa4x = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfa4x" )
## Components of Snow Crab status in Maritimes Region {.c}
- Life history
- Ecosystem change
- Human interactions
- Fishery statistics
- Other interactions
- Scientific survey results
- Life history X Ecosystem change X Human interactions = Complexity
# Life history {.c}
## Life history {.c}
```{r photos, echo=FALSE, out.width='30%', fig.align='center', fig.show='hold', fig.cap = 'Snow Crab pelagic Zoea, benthic male and mating pair. Note sexual dimorphism.' }
fn1=file.path( media_loc, "snowcrab_zoea.png" )
fn2=file.path( media_loc, "snowcrab_male.png" )
fn3=file.path( media_loc, "snowcrab_male_and_female.png" )
knitr::include_graphics( c(fn1, fn2, fn3) )
# \@ref(fig:photos)
Life history: stages{.c}
```{r lifehistory, echo=FALSE, out.width='90%', fig.align='center', fig.cap = 'Life history patterns of snow crab and approximate timing of the main life history stages of snow crab and size (carapace width; CW mm) and instar (Roman numerals). Size and timings are specific to the area of study and vary with environmental conditions, food availability and genetic variability.' } loc = file.path( Sys.getenv("HOME"), "projects", "dynamical_model", "snowcrab", "media" ) fn1=file.path( loc, "life_history.png" ) knitr::include_graphics( fn1 )
\@ref(fig:lifehistory)
## Life history: male growth stanzas {.c}
```{r lifehistory_male, echo=FALSE, out.width='50%', fig.align='center', fig.cap = 'The growth stanzas of the male component and decision paths to maturity and terminal moult. Black ellipses indicate terminally molted animals.' }
loc = file.path( Sys.getenv("HOME"), "projects", "dynamical_model", "snowcrab", "media" )
fn1=file.path( loc, "life_history_male.png" )
knitr::include_graphics( fn1 )
# \@ref(fig:lifehistory_male)
Life history: growth modes{.c}
```{r growth_modes, echo=FALSE, out.width='40%', fig.align='center', fig.cap = 'Modal analysis.' } loc = file.path( Sys.getenv("HOME"), "bio.data", "bio.snowcrab", "output" ) fn1=file.path( loc, "size_structure", "growth_summary.png" ) knitr::include_graphics( c(fn1) )
\@ref(fig:lifehistory_male)
## Life history: notable traits
- Sexual dimorphism
- Pelagic in larval stages; benthic in pre-adolescent and adult stages
- Biannual, annual molts depending upon size/age and environmental conditions
- Terminal molt to maturity and survives for up to 5 years
- Total life span: up to 15 years.
- Stenothermic (narrow) temperature requirements < 6 C
- Ontogenetic shifts in habitat preferences: warmer, complex substrates to mud.
- Cannibalism of immature crab by mature female snow crab are known
- Primiparous female (57.4 mm CW) produces between 35,000 to 46,000 eggs
- Multiparous females more fecund (>100,000 eggs)
- Eggs extruded February and April and brooded for up to two years (> 80% in SSE follow an annual cycle) and hatched/released from April to June.
## Life history: movement (mark-recapture) {.c}
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
```{r movementtracks, echo=FALSE, out.width='60%', fig.align='center', fig.show='hold', fig.cap = 'Snow Crab movement tracks from mark and recapture.' }
fn1=file.path( media_loc, "movement0.png" )
fn2=file.path( media_loc, "movement.png" )
knitr::include_graphics( c(fn1, fn2) )
# \@ref(fig:movementtracks)
\end{column}
\begin{column}{.48\textwidth} ```{r movement, echo=FALSE, out.width='55%', fig.align='center', fig.show='hold', fig.cap = 'Snow Crab movement distances and rates.' } fn1=file.path( media_loc, "snowcrab_movement_distances.png" ) fn2=file.path( media_loc, "snowcrab_movement_rates.png" ) knitr::include_graphics( c(fn1, fn2) )
\@ref(fig:movement)
\end{column}
\end{columns}
\end{small}
## Life history: clustering {.c}
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
```{r aggregation, echo=FALSE, out.width='60%', fig.align='center', fig.show='hold', fig.cap = 'Australian spider crab \\emph{Leptomithrax gaimardii} aggregation for moulting and migration and Alaska red king crab \\emph{Paralithodes camtschaticus} aggregation in Alaska for egg release, migrations.' }
fn1=file.path( media_loc, "australian_leptomithrax_gaimardii.png" )
fn2=file.path( media_loc, "kingcrab_aggregation.png" )
knitr::include_graphics( c(fn1, fn2 ) )
# \@ref(fig:aggregation)
-
Other crab species show "Mounding" for protection from predation (larval, moulting and females)
-
Narrow habitat preferences force them to move and cluster when environment is poor \end{column}
\begin{column}{.48\textwidth} ```{r clustering, echo=FALSE, out.width='90%', fig.align='center', fig.show='hold', fig.cap = 'High density locations of Snow Crab, approximately 1 per square meter.'} fn = file.path(p$project.outputdir, "maps", "map_highdensity_locations.png" ) knitr::include_graphics( fn )
\@ref(fig:aggregation)
if (0) { # high density locations directly from databases M = snowcrab.db( DS="set.complete", p=p ) setDT(M) i = which(M$totno.all > 2.5*10^5) H = M[i, .( plon, plat, towquality, dist, distance, surfacearea, vessel, yr, z, julian, no.male.all, no.female.all, cw.mean, totno.all, totno.male.imm, totno.male.mat, totno.female.imm, totno.female.mat, totno.female.primiparous, totno.female.multiparous, totno.female.berried)] H$log10density = log10(H$totno.all) library(ggplot2) cst = coastline_db( p=p, project_to=st_crs(pg) ) isodepths = c(100, 200, 300) isob = isobath_db( DS="isobath", depths=isodepths, project_to=st_crs(pg)) isob$level = as.factor( isob$level) plt = ggplot() + geom_sf( data=cst, show.legend=FALSE ) + geom_sf( data=isob, aes( alpha=0.1, fill=level), lwd=0.1, show.legend=FALSE) + geom_point(data=H, aes(x=plon, y=plat, colour=log10density), size=5) + coord_sf(xlim = c(270, 940 ), ylim = c(4780, 5200 )) + theme(legend.position="inside", legend.position.inside=c(0.08, 0.8)) png(filename=fn, width=1000,height=600, res=144) (plt) dev.off() }
\begin{block}{Uncertainty}
Historical snow crab high density locations
\end{block}
\end{column}
\end{columns}
\end{small}
# Ecosystem change
## Ecosystem change: Predators {.c}
- SSE: many species changes, snow crab are long-lived so interact with many of them
- Stomach samples: Atlantic Halibut, Atlantic Wolffish, Thorny Skate as primary predators
- Skewed sex ratios (few mature females): possibly linked to differential predation mortality (mature females being much smaller and full of fat-rich gonads and eggs).
- Halibut (DFO 2018) have significantly increased in abundance in the Region.
- Elevated co-ocurrence in areal **densities** with snow crab trawl samples means greater encounter rates
## Ecosystem change: Predators - Atlantic Halibut {.c}
```{r halibut-timeseries, out.width='50%', echo=FALSE, fig.align='center', fig.cap = 'Atlantic Halibut crude, unadjusted geometric mean numerical density (no/km$^2$) from annual Snow Crab survey. Error bars are 95\\% Confidence Intervals.' }
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.30.pdf') )
# \@ref(fig:halibut-timeseries)
Ecosystem change: Predators - Atlantic Halibut ... {.c}
```{r halibut-map, out.width='32%', fig.show='hold', fig.align='center', fig.cap= 'Halibut density log10(no/km$^2$) from the Snow Crab survey.' } loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.30' ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn4 = file.path( loc, paste( 'ms.no.30', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'ms.no.30', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'ms.no.30', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'ms.no.30', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:halibut-map)
\begin{block}{Uncertainty}
Higher predation mortality seems likely (more encounters with warmer-water species)
\end{block}
## Ecosystem change: Predators - Thorny skate {.c}
```{r thornyskate-timeseries, out.width='60%', echo=FALSE, fig.align='center', fig.cap = 'Thorny Skate crude, unadjusted geometric mean numerical density (no/km$^2$) from annual Snow Crab survey. Error bars are 95\\% Confidence Intervals.'}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.201.pdf') )
# \@ref(fig:thornyskate-timeseries)
Ecosystem change: Predators - Thorny skate ... {.c}
```{r thornyskate-map, out.width='32%', fig.show='hold', fig.align='center', fig.cap= 'Thorny skate density log10(no/km$^2$) from the Snow Crab survey.' } loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.201' ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn4 = file.path( loc, paste( 'ms.no.201', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'ms.no.201', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'ms.no.201', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'ms.no.201', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:thornyskate-map)
\begin{block}{Uncertainty}
Higher predation mortality seems likely (more encounters with warmer-water species)
\end{block}
<!--
~~~{=comment}
## Ecosystem change: Predators - Striped Atlantic Wolffish {.c}
```{r Wolffish-timeseries, out.width='60%', echo=FALSE, fig.align='center', fig.cap = 'Striped Atlantic Wolffish crude, unadjusted geometric mean numerical density (no/km$^2$) from annual Snow Crab survey. Error bars are 95\\% Confidence Intervals.' }
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.50.pdf') )
# \@ref(fig:Wolffish-timeseries)
Ecosystem change: Predators - Striped Atlantic Wolffish ... {.c}
```{r Wolffish-map, out.width='32%', fig.show='hold', fig.align='center', fig.cap= 'Striped Atlantic Wolffish density log10(no/km$^2$) from the Snow Crab survey.' } loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.50' ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn4 = file.path( loc, paste( 'ms.no.50', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'ms.no.50', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'ms.no.50', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'ms.no.50', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:Wolffish-map)
## Ecosystem change: Competitor - Lesser toad crab {.c}
```{r lessertoadcrab-timeseries, out.width='60%', echo=FALSE, fig.align='center', fig.cap = 'Lesser Toad Crab crude, unadjusted geometric mean numerical density (no/km$^2$) from annual Snow Crab survey. Error bars are 95\\% Confidence Intervals.' }
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.2521.pdf') )
# \@ref(fig:lessertoadcrab-timeseries)
Ecosystem change: Competitor - Lesser toad crab ... {.c}
```{r lessertoadcrab-map, out.width='32%', fig.show='hold', fig.align='center', fig.cap= 'Lesser Toad Crab density log10(no/km$^2$) from the Snow Crab survey.' } loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.2521' ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn4 = file.path( loc, paste( 'ms.no.2521', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'ms.no.2521', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'ms.no.2521', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'ms.no.2521', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:lessertoadcrab-map)
~~~
-->
## Ecosystem change: Co-occurring - Northern shrimp {.c}
```{r Shrimp-timeseries, out.width='60%', echo=FALSE, fig.align='center', fig.cap = 'Northern Shrimp crude, unadjusted geometric mean numerical density (n/$km^2$) from annual Snow Crab survey. Error bars are 95\\% Confidence Intervals.' }
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.2211.pdf') )
# \@ref(fig:Shrimp-timeseries)
Ecosystem change: Co-occurring - Northern shrimp ... {.c}
```{r Shrimp-map, out.width='32%', fig.show='hold', fig.align='center', fig.cap= 'Northern Shrimp density log10(no/km$^2$) from the Snow Crab survey.' } loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.2211' ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn4 = file.path( loc, paste( 'ms.no.2211', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'ms.no.2211', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'ms.no.2211', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'ms.no.2211', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:Shrimp-map)
Shrimp with similar habitat preferences have declined, possibly due to large-scaled habitat variations and predation.
\begin{block}{Uncertainty}
Sampling was incomplete in 2020 and 2022 in S-ENS.
\end{block}
## Ecosystem change: species composition
```{r speciesomposition, echo=FALSE, out.width='48%', fig.align='center', fig.show='hold', fig.cap = 'Species composition in space and time. Primary gradient is related to bottom temperatures.' }
spc_loc = file.path( data_root, 'aegis', 'speciescomposition', 'modelled', 'default', 'maps' )
fn1 = file.path( spc_loc, 'pca1.space_re_total.png')
fn2 = file.path( spc_loc, 'pca2.space_re_total.png')
ts_loc = file.path( data_root, 'aegis', 'speciescomposition', 'modelled', 'default', 'figures' )
fn3 = file.path( ts_loc, 'pca1_time.png')
fn4 = file.path( ts_loc, 'pca2_time.png')
knitr::include_graphics( c(fn1, fn3 ) )
# \@ref(fig:habitat3)
\begin{block}{Uncertainty} Sampling was incomplete in 2020 and 2022 in S-ENS. \end{block}
Ecosystem change: Bottom Temperature {.c}
```{r bottom-temperatures-survey, out.width='50%', echo=FALSE, fig.align='center', fig.cap = 'Annual variations in bottom temperature observed during the Snow Crab survey. The horizontal (black) line indicates the long-term, median temperature within each subarea. Error bars represent standard errors.' } knitr::include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 't.pdf') )
\@ref(fig:bottom-temperatures-survey)
\begin{block}{Uncertainty}
Sampling was incomplete in 2020 and 2022 in S-ENS.
\end{block}
## Ecosystem change: Bottom Temperature ... {.c}
- Average bottom temperatures **observed** in the 2022 Snow Crab survey were near or above historical highs in all areas
- Temperatures are more stable in N-ENS than S-ENS; 4X exhibits the most erratic and highest annual mean bottom temperatures.
- Observed temperatures in the 2022 Snow Crab survey for S-ENS increased well above the average.
- Average temperature increased well beyond the $7^\circ$C threshold in 4X. N-ENS and S-ENS also continued to experience historical highs in bottom temperature and elevated spatial variability of bottom temperatures.
## Ecosystem considerations: Bottom Temperature ... {.c}
```{r bottom-temperatures-map, out.width='30%', echo=FALSE, fig.show='hold', fig.align='center', fig.cap = 'Spatial variations in bottom temperature estimated from a historical analysis of temperature data for 1 September.' }
loc = file.path( data_root, 'aegis', 'temperature', 'modelled', 'default', 'maps' )
yrsplot = year.assessment + c(0:-10)
fn10 = file.path( loc, paste( 'predictions.', yrsplot[10], '.0.75', '.png', sep='') )
fn9 = file.path( loc, paste( 'predictions.', yrsplot[9], '.0.75', '.png', sep='') )
fn8 = file.path( loc, paste( 'predictions.', yrsplot[8], '.0.75', '.png', sep='') )
fn7 = file.path( loc, paste( 'predictions.', yrsplot[7], '.0.75', '.png', sep='') )
fn6 = file.path( loc, paste( 'predictions.', yrsplot[6], '.0.75', '.png', sep='') )
fn5 = file.path( loc, paste( 'predictions.', yrsplot[5], '.0.75', '.png', sep='') )
fn4 = file.path( loc, paste( 'predictions.', yrsplot[4], '.0.75', '.png', sep='') )
fn3 = file.path( loc, paste( 'predictions.', yrsplot[3], '.0.75', '.png', sep='') )
fn2 = file.path( loc, paste( 'predictions.', yrsplot[2], '.0.75', '.png', sep='') )
fn1 = file.path( loc, paste( 'predictions.', yrsplot[1], '.0.75', '.png', sep='') )
knitr::include_graphics( c( fn3, fn2, fn1) )
# \@ref(fig:bottom-temperatures-map)
# *Spatial variations in bottom temperature estimated from a historical analysis of temperature data for 1 September.*
\begin{block}{Uncertainty} * Groundfish surveys were not conducted in 2020 and 2022 in the snow crab domain.
- Snow crab surveys were not conducted in 2020, and incomplete in 2022 for S-ENS. \end{block}
Ecosystem considerations: Bottom Temperature ... {.c}
Persistent spatial gradient of almost $15^\circ$C in bottom temperatures in the Maritimes Region.
Variable due to confluence of the warm, high salinity Gulf Stream from the S-SE along the shelf edge; cold, low salinity Labrador Current; and cold low salinity St. Lawrence outflow from the N-NE, as well as a nearshore Nova Scotia current, running from the NE.
```{r bottom-temperatures-spatialeffect, out.width='35%', echo=FALSE, fig.align='center', fig.cap = 'Persistent spatial effect of bottom temperature, relative to the overall mean, after adjustment for spatiotemporal variability and autocorrelations. Time period from 1999 to present.' } loc = file.path( data_root, 'aegis', 'temperature', 'modelled', 'default', 'maps' ) knitr::include_graphics( file.path( loc, 'space_re_total.png') )
\@ref(fig:bottom-temperatures-spatialeffect)
## Ecosystem considerations: Bottom Temperature ... {.c}
\begin{columns}
\begin{column}{.6\textwidth}
```{r bottom-temperatures, out.width='65%', echo=FALSE, fig.align='center', fig.cap = '' }
knitr::include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'temperature_bottom.pdf') )
# \@ref(fig:bottom-temperatures)
\end{column} \begin{column}{.4\textwidth}
\vspace{12mm}
\begin{footnotesize} \textbf{Figure}: Temporal variations in bottom temperature estimated from a historical analysis of temperature data. Red horizontal line is at $7^\circ$C. Presented are 95\% Credible Intervals of spatial variability in temperature at each time slice, after adjustment for spatiotemporal autocorrelation. \end{footnotesize} \end{column} \end{columns}
Human interactions
Human interactions: Every known population is exploited worldwide {.c}
- Human consumption (males; > 95mm CW) due to higher meat yield and sexual dimorphism
- Bait in other fisheries (illegal in Canada, but still present)
- Fertilizer in agriculture
- Chitosan: glucosamine polysaccharide derived from chitin
- agent to slow bleeding from wounds (Zhang et al. 2015, Moghadas et al. 2016)
- agriculturally as natural fungicides and bactericides (Linden & Stoner 2007)
- plastic replacement (Tampieri et al. 2003)
- battery electrolyte (Poosapati et al. 2021).
Human interactions: Management Approach
\begin{columns} \begin{column}{.46\textwidth} \begin{tiny} ```{r area_map, echo=FALSE, out.width='80%', fig.align='center', fig.cap = 'The Scotian Shelf (NW Atlantic Ocean; NAFO Div. 4VWX). Shown are isobaths and major bathymetric features. Managed Crab Fishing Areas (CFAs; divided by dashed lines) include: NENS, SENS, 4X. SENS is further subdivided (dotted line) into 23 (NW) and 24 (SE).' } loc = file.path( Sys.getenv("HOME"), "projects", "dynamical_model", "snowcrab", "media" ) fn1=file.path( loc, "area_map.png" ) knitr::include_graphics( fn1 )
\@ref(fig:area_map)
\end{tiny}
\end{column}
\begin{column}{.52\textwidth}
\begin{footnotesize}
\begin{itemize}
\item Precautionary Approach, Fish Stock Provisions, 2022
\item Spatial refugia (slope edge, MPAs)
\item Temporal refugia (fishing seasons)
\item Biological refugia: most life stages protected
\begin{itemize}
\begin{scriptsize}
\item Conservative exploitation since mid-2000s
\item Spawning stock legally and completely protected
\item Market-driven protection for 10+ yrs
\end{scriptsize}
\end{itemize}
\item Evidence-based decision making: trawl survey, assessment
\item Distributed knowledge network: traditional, historical, scientific
\item Satellite VMS; biodegradeable mesh (ghost-fishing); weighted lines (entanglement), etc ...
\end{itemize}
\end{footnotesize}
\end{column}
\end{columns}
## Human interactions: Fishing effort {.c}
Similar between `r year.assessment` and `r year_previous` in terms of spatial distribution. In S-ENS, there was, however, a minor spatial contraction to inshore areas and away from the area 23-24 boundary.
\begin{tiny}
```{r effort-map, echo=FALSE, out.width='45%', fig.show='hold', fig.align='center', fig.cap = 'Snow Crab fishing effort from fisheries logbook data for previous and current years. Units are No. $\\times 10^3$ per (10 km X 10 km) grid.' }
loc0= file.path( SCD, "output", "maps", "logbook", "snowcrab", "annual", "effort" )
fn1 = file.path( loc0, paste( "effort", year_previous, "png", sep=".") )
fn2 = file.path( loc0, paste( "effort", year.assessment, "png", sep=".") )
include_graphics( c(fn1, fn2) )
# \@ref(fig:landings-map)
\end{tiny}
Human interactions: Fishing effort ... {.c}
\begin{tiny} ```{r effort-timeseries, echo=FALSE, out.width='60%', fig.align='center', fig.cap = 'Temporal variations in fishing effort $\times 10^3$ trap hauls.' } fn1=file.path( SCD, "assessments", year.assessment, "timeseries", "fishery", "effort.ts.pdf" ) knitr::include_graphics( fn1 )
\@ref(fig:effort-timeseries)
\end{tiny}
<!--
```{r table-fishery-nens, echo=FALSE, eval = FALSE }
ii = which(dt$Region=="cfanorth")
oo = dt[ii, c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
kable( oo, format="simple", row.names=FALSE, align="cccccc",
caption = "Fishery performance statistics in N-ENS. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th).")
# \@ref(tab:table-fishery-nens)
```
---
```{r table-fishery-sens, echo=FALSE, eval = FALSE }
ii = which(dt$Region=="cfasouth")
oo = dt[ii, c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
kable( oo, format="simple", row.names=FALSE, align="cccccc",
caption = "Fishery performance statistics in S-ENS. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th).")
# \@ref(tab:table-fishery-nens)
```
---
```{r table-fishery-4x, echo=FALSE, eval = FALSE }
ii = which(dt$Region=="cfa4x")
oo = dt[ii,c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
kable(oo, format="simple", row.names=FALSE, align="cccccc",
caption = "Fishery performance statistics in 4X. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th). There were no landings or TACs in 2018/2019 due to indications of low abundance.")
# \@ref(tab:table-fishery-4x)
```
-->
## Human interactions: Fishery landings and TACs {.c}
- The landings in N-ENS for 2022 and 2021 were similar in their spatial patterns.
- The landings in 4X for 2022 were spatially more contracted than 2021.
\begin{tiny}
```{r landings-map, echo=FALSE, out.width='45%', fig.show='hold', fig.align='center', fig.cap = 'Snow Crab landings from fisheries logbook data for previous and current years. Units are tons per 10 km x 10 km grid.' }
loc0= file.path( SCD, "output", "maps", "logbook", "snowcrab", "annual", "landings" )
fn1 = file.path( loc0, paste( "landings", year_previous, "png", sep=".") )
fn2 = file.path( loc0, paste( "landings", year.assessment, "png", sep=".") )
knitr::include_graphics( c(fn1, fn2 ) )
# \@ref(fig:landings-map)
\end{tiny}
Human interactions: Fishery landings and TACs ... {.c}
In 2022, landings in all areas were below respective TACs.
\begin{tiny} ```{r landings-timeseries, echo=FALSE, out.width='60%', fig.align='center', fig.cap = 'Landings (t) of Snow Crab on the SSE. For 4X, the year refers to the starting year of the season. Inset is a closeup view of the timeseries for N-ENS and 4X.'} include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "fishery", "landings.ts.pdf" ) )
\@ref(fig:landings-timeseries)
\end{tiny}
## Human interactions: Fishery catch rates
- Generally, the spatial extent of exploitation was smaller, many of the exploited area show elevated catch rates,
- In 4X catch rates were lower in 2022.
```{r cpue-map, echo=FALSE, out.width='45%', fig.show='hold', fig.align='center', fig.cap = 'Snow Crab crude catch rates on the Scotian Shelf for previous and current years. Units are kg/trap haul per 10 km x 10 km grid.' }
loc0= file.path( SCD, "output", "maps", "logbook", "snowcrab", "annual", "cpue" )
fn1 = file.path( loc0, paste( "cpue", year_previous, "png", sep=".") )
fn2 = file.path( loc0, paste( "cpue", year.assessment, "png", sep=".") )
knitr::include_graphics( c(fn1, fn2 ) )
# \@ref(fig:cpue-map)
Human interactions: Fishery catch rates ...
\begin{tiny} ```{r cpue-timeseries, echo=FALSE, out.width='60%', fig.align='center', fig.cap = 'Temporal variations in crude catch rates of Snow Crab (kg per trap haul).'} include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "fishery", "cpue.ts.pdf" ) )
\@ref(fig:cpue-timeseries)
\end{tiny}
## Human interactions: At-Sea-Observed information
- Target: 5% of landings
- In 2021, both N-ENS and 4X were not sampled by At-Sea-Observers.
- In 2022, ~ 0.8 % of landings in 4X were sampled by At-Sea-Observers
\begin{tiny}
```{r observer-locations-map, out.width='22%', fig.show='hold', fig.align='center', fig.cap= 'Snow Crab At-sea-observer locations.' }
loc = file.path( SCD, "output", "maps", "observer.locations" )
yrsplot = year.assessment + c(0:-4)
fn4 = file.path( loc, paste( "observer.locations", yrsplot[4], "png", sep=".") )
fn3 = file.path( loc, paste( "observer.locations", yrsplot[3], "png", sep=".") )
fn2 = file.path( loc, paste( "observer.locations", yrsplot[2], "png", sep=".") )
fn1 = file.path( loc, paste( "observer.locations", yrsplot[1], "png", sep=".") )
include_graphics( c( fn4, fn3, fn2, fn1) )
# \@ref(fig:observer-locations-map)
\end{tiny}
\vspace{5mm}
Bycatch: last assessment was in 2017 and levels were << 1% by weight.
Stock status: Carapace condition of mature male crab
\begin{small} \begin{columns} \begin{column}{.48\textwidth}
\vspace{4mm}
- CC5 crab of mature crab.
\vspace{12mm}
\begin{scriptsize} \textbf{Figure}: Size-frequency of mature male Snow Crab by carapace width (mm) and carapace condition from surveys. Columns are years and rows are N-ENS (top), S-ENS(middle) and 4X(bottom). \end{scriptsize}
\end{column} \begin{column}{.48\textwidth} \begin{tiny} ```{r sizefeq-male-survey-cc, out.width='45%', fig.show='hold', echo=FALSE, fig.align='center', fig.cap = ''} odir = file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "carapacecondition" ) fn1 = file.path( odir, "sizefreq.cfanorth.2019.pdf" ) fn2 = file.path( odir, "sizefreq.cfasouth.2019.pdf" ) fn3 = file.path( odir, "sizefreq.cfa4x.2019.pdf" ) fn4 = file.path( odir, "sizefreq.cfanorth.2021.pdf" ) fn5 = file.path( odir, "sizefreq.cfasouth.2021.pdf" ) fn6 = file.path( odir, "sizefreq.cfa4x.2021.pdf" ) fn7 = file.path( odir, "sizefreq.cfanorth.2022.pdf" ) fn8 = file.path( odir, "sizefreq.cfasouth.2022.pdf" ) fn9 = file.path( odir, "sizefreq.cfa4x.2022.pdf" ) include_graphics(c( fn4, fn7, fn5, fn8, fn6, fn9) )
\@ref(fig:sizefeq-male-survey-cc)
\end{tiny}
\end{column}
\end{columns}
\end{small}
## Stock status: Recruitment
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
\vspace{4mm}
\begin{itemize}
\item Little to no recruitment is expected for the next 1-3 years in N-ENS.
\item Moderate levels of recruitment are expected in S-ENS.
\item Low to moderate levels of recruitment are expected for 2 years in 4X.
\end{itemize}
\vspace{4mm}
\begin{scriptsize}
\textbf{Figure}: Size-frequency (areal density; no/km$^2$) histograms by carapace width of male Snow Crab. The vertical line represents the legal size (95 mm). Immature animals are shown with light coloured bars, mature with dark.
\end{scriptsize}
\end{column}
\begin{column}{.48\textwidth}
```{r sizefeq-male, out.width='90%', echo=FALSE, fig.align='center', fig.cap = ''}
include_graphics( file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "survey", "male.denl.png" ) )
# \@ref(fig:sizefeq-male)
\end{column} \end{columns} \end{small}
Stock status: Reproduction
\begin{small} \begin{columns} \begin{column}{.48\textwidth}
\begin{itemize} \item All areas had recruitment of female crab into the mature (egg-bearing) segment of the population from 2016-2022. \item In N-ENS for 2022, a decline in numerical densities, and low densities of adolescent females. \item Egg and larval production is expected to be moderate to high in the next year in all areas except N-ENS. \end{itemize}
\vspace{2mm} \begin{scriptsize} \textbf{Figure}: Size-frequency (areal density; no/km$^2$) histograms by carapace width of female Snow Crab. Immature animals are shown with light coloured bars, mature with dark. \end{scriptsize}
\end{column} \begin{column}{.48\textwidth} ```{r sizefeq-female, out.width='90%', echo=FALSE, fig.align='center', fig.cap = ''} include_graphics( file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "survey", "female.denl.png" ) )
\@ref(fig:sizefeq-female)
\end{column}
\end{columns}
\end{small}
## Stock status: Reproduction ...
```{r fmat-timeseries, out.width='50%', echo=FALSE, fig.align='center', fig.cap = 'Mature female density log$_{10}$(no/km$^2$) from the Snow Crab survey.' }
include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "survey", "totno.female.mat.pdf") )
# \@ref(fig:fmat-timeseries)
Stock status: Mature female
Distributions are heterogeneous and often in shallower areas.
```{r fmat-map, echo=FALSE, out.width='32%', fig.show='hold', fig.align='center', fig.cap= 'Mature female density log$_{10}$(no/km$^2$) from the Snow Crab survey.' } loc = file.path( SCD, "output", "maps", "survey", "snowcrab", "annual", "totno.female.mat" ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn4 = file.path( loc, paste( "totno.female.mat", yrsplot[4], "png", sep=".") ) fn3 = file.path( loc, paste( "totno.female.mat", yrsplot[3], "png", sep=".") ) fn2 = file.path( loc, paste( "totno.female.mat", yrsplot[2], "png", sep=".") ) fn1 = file.path( loc, paste( "totno.female.mat", yrsplot[1], "png", sep=".") ) include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:fmat-map)
## Stock status: Viable Habitat {.t}
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
```{r habitat, echo=FALSE, out.width='52%', fig.align='center', fig.show='hold', fig.cap = 'Persistent habitat.' }
fn1=file.path( media_loc, "viable_habitat.png" )
knitr::include_graphics( c(fn1 ) )
# \@ref(fig:habitat)
\end{column} \begin{column}{.48\textwidth} ```{r habitat2, echo=FALSE, out.width='40%', fig.align='center', fig.show='hold', fig.cap = 'Habitat preferences.' } fn2=file.path( media_loc, "viable_habitat_depth_temp.png" ) knitr::include_graphics( c( fn2 ) )
\@ref(fig:habitat2)
\end{column}
\end{columns}
\end{small}
<!--
## Stock status: Viable Habitat ... {.t}
\vspace{6mm}
```{r habitat3, echo=FALSE, eval = FALSE, out.width='30%', fig.align='center', fig.show='hold', fig.cap = 'Persistent habitat.' }
fn1=file.path( media_loc, "vh_i.png" )
fn2=file.path( media_loc, "vh_mf.png" )
fn3=file.path( media_loc, "vh_mm.png" )
knitr::include_graphics( c(fn1, fn2, fn3 ) )
# \@ref(fig:habitat3)
-->
Stock status: Viable Habitat ... {.c}
```{r fb-habitat-map, out.width='30%', fig.show='hold', fig.align='center', fig.cap= 'Habitat viability (probability; fishable Snow Crab).' }
loc = file.path( SCD, 'modelled', 'default_fb', 'predicted_habitat' ) vn = "habitat." yrsplot = year.assessment + c(0:-10) fn10 = file.path( loc, paste( vn, yrsplot[10], '.png', sep='') ) fn9 = file.path( loc, paste( vn, yrsplot[9], '.png', sep='') ) fn8 = file.path( loc, paste( vn, yrsplot[8], '.png', sep='') ) fn7 = file.path( loc, paste( vn, yrsplot[7], '.png', sep='') ) fn6 = file.path( loc, paste( vn, yrsplot[6], '.png', sep='') ) fn5 = file.path( loc, paste( vn, yrsplot[5], '.png', sep='') ) fn4 = file.path( loc, paste( vn, yrsplot[4], '.png', sep='') ) fn3 = file.path( loc, paste( vn, yrsplot[3], '.png', sep='') ) fn2 = file.path( loc, paste( vn, yrsplot[2], '.png', sep='') ) fn1 = file.path( loc, paste( vn, yrsplot[1], '.png', sep='') ) include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:fb-habitat-map)
Figure XXX. Habitat viability (probability; fishable Snow Crab)
## Stock status: Viable Habitat ... {.c}
```{r fb-habitat-timeseries, out.width='50%', echo=FALSE, fig.align='center', fig.cap = 'Habitat viability (probability; fishable Snow Crab). Means and 95\\% Credible Intervals are presented.' }
loc = file.path( SCD, 'modelled', 'default_fb', 'aggregated_habitat_timeseries' )
include_graphics( file.path( loc, 'habitat_M0.png') )
# \@ref(fig:fb-habitat-timeseries)
Stock status: Sex ratios (proportion female, mature)
\begin{small} \begin{columns} \begin{column}{.48\textwidth}
\vspace{2mm} \begin{itemize} \item Mostly male-dominated: larger size may be protective against predation? \item Imbalance indicates differential mortality: predation, competition and fishing \item In 4X, sex ratios are balanced. \end{itemize} \end{column} \begin{column}{.48\textwidth} ```{r sexratio-mature, out.width='60%', echo=FALSE, fig.align='center', fig.cap = 'Timeseries of sex ratios.' } include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "survey", "sexratio.mat.pdf") )
\@ref(fig:sexratio-mature)
\end{column}
\end{columns}
\end{small}
```{r sexratio-map, echo=FALSE, out.width='25%', fig.show='hold', fig.align='center', fig.cap= 'Map of sex ratios.'}
yrsplot = setdiff( year.assessment + c(0:-4), 2020)
loc = file.path( SCD, "output", "maps", "survey", "snowcrab", "annual", "sexratio.mat" )
fn4 = file.path( loc, paste( "sexratio.mat", yrsplot[4], "png", sep=".") )
fn3 = file.path( loc, paste( "sexratio.mat", yrsplot[3], "png", sep=".") )
fn2 = file.path( loc, paste( "sexratio.mat", yrsplot[2], "png", sep=".") )
fn1 = file.path( loc, paste( "sexratio.mat", yrsplot[1], "png", sep=".") )
include_graphics( c( fn3, fn2, fn1 ) )
# \@ref(fig:sexratio-map)
Stock status: Biomass Density {.c}
Note that high and low biomass density areas fluctuate with time
\begin{tiny} ```{r fbgeomean-map, echo=FALSE, out.width='30%', fig.show='hold', fig.align='center', fig.cap= 'Snow Crab survey fishable component biomass density log~10(t/km$^2$). Note, there is no data in 2020.' } loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'R0.mass') yrsplot = setdiff(year.assessment + c(0:-9), 2020 ) fn6 = file.path( loc, paste( 'R0.mass', yrsplot[6], 'png', sep='.') ) fn5 = file.path( loc, paste( 'R0.mass', yrsplot[5], 'png', sep='.') ) fn4 = file.path( loc, paste( 'R0.mass', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'R0.mass', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'R0.mass', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'R0.mass', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) )
\end{tiny}
## Stock status: Biomass Density ... {.c}
- A peak in 2009 to 2014 and has since been declining in all areas.
\begin{tiny}
```{r fbGMTS, out.width='50%', echo=FALSE, fig.align='center', fig.cap = 'The crude, unadjusted geometric mean fishable biomass density log~10(t/km$^2$) from the Snow Crab survey. Error bars represent 95\\% Confidence Intervals. Note the absence of data in 2020. Prior to 2004, surveys were conducted in the Spring.'}
fn = file.path(SCD,'assessments', year.assessment, 'timeseries','survey','R0.mass.pdf')
include_graphics( c(fn) )
#\@ref(fig:fbGMTS)
\end{tiny}
Stock status: Biomass Index (aggregate)
A contraction of spatial range in 4X and the western parts of S-ENS were also evident in 2021 to 2022.
```{r fbindex-map, echo=FALSE, out.width='30%', fig.show='hold', fig.align='center', fig.cap= 'Biomass index log~10(t/km$^2$) predicted from the Snow Crab survey.' } loc = file.path( SCD, 'modelled', 'default_fb', 'predicted_biomass_densities' ) yrsplot = year.assessment + c(0:-10) fn10 = file.path( loc, paste( 'biomass', yrsplot[10], 'png', sep='.') ) fn9 = file.path( loc, paste( 'biomass', yrsplot[9], 'png', sep='.') ) fn8 = file.path( loc, paste( 'biomass', yrsplot[8], 'png', sep='.') ) fn7 = file.path( loc, paste( 'biomass', yrsplot[7], 'png', sep='.') ) fn6 = file.path( loc, paste( 'biomass', yrsplot[6], 'png', sep='.') ) fn5 = file.path( loc, paste( 'biomass', yrsplot[5], 'png', sep='.') ) fn4 = file.path( loc, paste( 'biomass', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'biomass', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'biomass', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'biomass', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) )
## Stock status: Biomass Index (aggregate) ... {.c}
\begin{tiny}
```{r fbindex-timeseries, out.width='60%', echo=FALSE, fig.align='center', fig.cap = 'The fishable biomass index (t) predicted by CARSTM of Snow Crab survey densities. Error bars represent Bayesian 95\\% Credible Intervals. Note large errors in 2020 when there was no survey.' }
include_graphics( file.path( SCD, 'modelled', 'default_fb', 'aggregated_biomass_timeseries' , 'biomass_M0.png') )
# \@ref(fig:fbindex-timeseries)
\end{tiny}
Stock status: Modelled Biomass (pre-fishery) ... {.c}
\begin{tiny} ```{r logisticPredictions, out.width='32%', echo=FALSE, fig.show='hold', fig.align='center', fig.cap = 'Model 1 fishable, posterior mean modelled biomass (pre-fishery; kt) are shown in dark orange for N-ENS, S-ENS and 4X (left, middle and right). Light orange are posterior samples of modelled biomass (pre-fishery; kt) to illustrate the variability of the predictions. The biomass index (post-fishery, except prior to 2004) after model adjustment by the model catchability coefficient is in gray.' } loc = file.path( SCD, 'fishery_model', year.assessment, 'logistic_discrete_historical' ) fn1 = file.path( loc, 'plot_predictions_cfanorth.pdf' ) fn2 = file.path( loc, 'plot_predictions_cfasouth.pdf' ) fn3 = file.path( loc, 'plot_predictions_cfa4x.pdf' ) include_graphics(c(fn1, fn2, fn3) )
\@ref(fig:logisticPredictions)
\end{tiny}
<!--
## Stock status: Fishing Mortality {.c}
N-ENS: #r round(FM_north[t0],3)` (annual exploitation rate of #r round(100*(exp(FM_north[t0])-1),2)`%) in #r year.assessment`
- Up from the #r year_previous` rate of #r round(FM_north[t1],3)` (annual exploitation rate of #r round(100*(exp(FM_north[t1])-1),1)`%)
S-ENS: #r round(FM_south[t0],3)` (annual exploitation rate of #r round(100*(exp(FM_south[t0])-1),1)`%) in #r year.assessment`
- Decreasing marginally from the #r year_previous` rate of #r round(FM_south[t1],3)` (annual exploitation rate of #r round(100*(exp(FM_south[t1])-1),1)`%)
4X: #r round(FM_4x[t0],3)` (annual exploitation rate of #r round(100*(exp(FM_4x[t0])-1),1)`%) in #r year.assessment`-#r year.assessment+1` season
- Decreasing from the #r year.assessment-1`-#r year.assessment` season rate of #r round(FM_4x[t1],3)` (annual exploitation rate of #r round(100*(exp(FM_4x[t1])-1),1)`%)
Localized exploitation rates are likely higher, as not all areas for which biomass is estimated are fished.
-->
## Stock status: Fishing Mortality ... {.c}
```{r logisticFishingMortality, out.width='32%', echo=FALSE, fig.show='hold', fig.align='center', fig.cap = 'Time-series of modelled instantaneous fishing mortality from Model 1, for N-ENS (left), S-ENS (middle), and 4X (right). Samples of the posterior densities are presented, with the darkest line being the mean.' }
odir = file.path( fishery_model_results, year.assessment, "logistic_discrete_historical" )
fn1 = file.path( odir, "plot_fishing_mortality_cfanorth.pdf" )
fn2 = file.path( odir, "plot_fishing_mortality_cfasouth.pdf" )
fn3 = file.path( odir, "plot_fishing_mortality_cfa4x.pdf" )
include_graphics(c(fn1, fn2, fn3) )
# \@ref(fig:logisticFishingMortality)
Stock status: Reference Points {.c}
```{r ReferencePoints, out.width='40%', echo=FALSE, fig.align='center', fig.cap = 'Harvest control rules for the Scotian Shelf Snow Crab fisheries.' } include_graphics( file.path( params$media_loc, 'harvest_control_rules.png') )
\@ref(fig:ReferencePoints)
## Stock status: Reference Points ... {.c}
```{r logistic-hcr, out.width='32%', echo=FALSE, fig.show='hold', fig.align='center', fig.cap = 'Reference Points (fishing mortality and modelled biomass) from Model 1, for N-ENS (left), S-ENS (middle), and 4X (right). The large yellow dot indicates most recent year and the 95\\% CI.' }
odir = file.path( fishery_model_results, year.assessment, "logistic_discrete_historical" )
fn1 = file.path( odir, 'plot_hcr_cfanorth.pdf' )
fn2 = file.path( odir, 'plot_hcr_cfasouth.pdf' )
fn3 = file.path( odir, 'plot_hcr_cfa4x.pdf' )
include_graphics(c(fn1, fn2, fn3) )
# \@ref(fig:logistic-hcr)
- N-ENS is in the "healthy" zone
- S-ENS is in the "healthy" zone
- 4X is in the "cautious" zone
References
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Banerjee, S., Carlin, B. P., and Gelfand, A. E.. 2004. Hierarchical Modeling and Analysis for Spatial Data. Monographs on Statistics and Applied Probability. Chapman and Hall/CRC.
Besag, Julian. 1974. Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society Series B (Methodological) 1974: 192-236.
Canada Gazette. 2022. Regulations Amending the Fishery (General) Regulations. Part II, Volume 156, Number 8.
Canada Gazette. 2016. St. Anns Bank Marine Protected Area Regulations. Canada Gazette, Part I, Vol 150, Issue 51: 4143-4149.
Choi, J.S. 2020. A Framework for the assessment of Snow Crab (Chioneocete opilio) in Maritimes Region (NAFO Div 4VWX) . DFO Can. Sci. Advis. Sec. Res. Doc. 2020/nnn. v + xxx p.
Choi, J.S. 2022. Reconstructing the Decline of Atlantic Cod with the Help of Environmental Variability in the Scotian Shelf of Canada. bioRxiv. https://doi.org/10.1101/2022.05.05.490753.
Choi, J. S., and B. C. Patten. 2001. Sustainable Development: Lessons from the Paradox of Enrichment. Ecosystem Health 7: 163–77.
Choi, Jae S., B. Cameron, K. Christie, A. Glass, and E. MacEachern. 2022. Temperature and Depth Dependence of the Spatial Distribution of Snow Crab. bioRxiv. https://doi.org/10.1101/2022.12.20.520893.
\end{tiny}
References ...
\begin{tiny}
DFO. 2018. Stock Status Update of Atlantic Halibut (Hippoglossus hippoglossus) on the Scotian Shelf and Southern Grand Banks in NAFO Divisions 3NOPs4VWX5Zc. DFO Can. Sci. Advis. Sec. Sci. Resp. 2018/022.
Hebert M, Miron G, Moriyasu M, Vienneau R, and DeGrace P. Efficiency and ghost fishing of Snow Crab (Chionoecetes opilio) traps in the Gulf of St. Lawrence. Fish Res. 2001; 52(3): 143-153. 10.1016/S0165-7836(00)00259-9
Riebler, A., Sørbye, S.H., Simpson D., and Rue, H. 2016. An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical methods in medical research 25: 1145-1165.
Simpson, D., Rue, H., Riebler, A., Martins, T.G., and Sørbye, SH. 2017. Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors. Statist. Sci. 32: 1-28.
\end{tiny}
END
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